class_docstr: AutoGluon model wrapper to the TabPFN model: https://github.com/automl/TabPFN
  
  Paper: "TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second"
  Authors: Noah Hollmann, Samuel Müller, Katharina Eggensperger, and Frank Hutter
  
  TabPFN is a viable model option when inference speed is not a concern,
  and the number of rows of training data is less than 10,000.
  
  Additionally, TabPFN is only available for classification tasks with up to 10 classes and 100 features.
  
  To use this model, `tabpfn` must be installed.
  To install TabPFN, you can run `pip install autogluon.tabular[tabpfn]` or `pip install tabpfn`.
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code: def __init__(self, **kwargs):
      super().__init__(**kwargs)
      self._feature_generator = None
  
file_path: autogluon/tabular/src/autogluon/tabular/models/tabpfn/tabpfn_model.py
imports: import numpy
  import pandas
  from autogluon.core.constants import BINARY, MULTICLASS
  from autogluon.core.models import AbstractModel
  from autogluon.core.utils import generate_train_test_split
  from autogluon.features.generators import LabelEncoderFeatureGenerator
label_desc: Use image node_id 1 to create a new TabPFNModel object from inherited base classes: AbstractModel with example: obj = TabPFNModel()